Advances in computer vision systems and feature detectors (e.g., machine learning based feature detectors such as neural networks) are leading to accelerated development of autonomous driving and related mapping/navigation services. However, feature detectors traditionally require significant computing resources to implement, with resource requirements increasing further as the number and variety of detectable features increase. Accordingly, service providers and vehicle manufacturers face significant technical challenges to providing in-vehicle feature detectors that balance feature detection performance and capability against the typically resource-constrained environment of in-vehicle embedded systems.